A PC-based data acquisition system for sub-atomic physics measurements

Modern particle physics measurements are heavily dependent upon automated data acquisition systems (DAQ) to collect and process experiment-generated information. One research group from the University of Saskatchewan utilizes a DAQ known as the Lucid data acquisition and analysis system. This thesis examines the project undertaken to upgrade the hardware and software components of Lucid. To establish the effectiveness of the system upgrades, several performance metrics were obtained including the system's dead time and input/output bandwidth.Hardware upgrades to Lucid consisted of replacing its aging digitization equipment with modern, faster-converting Versa-Module Eurobus (VME) technology and replacing the instrumentation processing platform with common, PC hardware. The new processor platform is coupled to the instrumentation modules via a fiber-optic bridging-device, the sis1100/3100 from Struck Innovative Systems.The software systems of Lucid were also modified to follow suit with the new hardware. Originally constructed to utilize a proprietary real-time operating system, the data acquisition application was ported to run under the freely available Real-Time Executive for Multiprocessor Systems (RTEMS). The device driver software provided with sis1100/3100 interface also had to be ported for use under the RTEMS-based system. Performance measurements of the upgraded DAQ indicate that the dead time has been reduced from being on the order of milliseconds to being on the order of several tens of microseconds. This increased capability means that Lucid's users may acquire significantly more data in a shorter period of time, thereby decreasing both the statistical uncertainties and data collection duration associated with a given experiment

Bio-Inspired Tools for a Distributed Wireless Sensor Network Operating System

The problem which I address in this thesis is to find a way to organise and manage a network of wireless sensor nodes using a minimal amount of communication. To find a solution I explore the use of Bio-inspired protocols to enable WSN management while maintaining a low communication overhead. Wireless Sensor Networks (WSNs) are loosely coupled distributed systems comprised of low-resource, battery powered sensor nodes. The largest problem with WSN management is that communication is the largest consumer of a sensor node’s energy. WSN management systems need to use as little communication as possible to prolong their operational lifetimes. This is the Wireless Sensor Network Management Problem. This problem is compounded because current WSN management systems glue together unrelated protocols to provide system services causing inter-protocol interference. Bio-inspired protocols provide a good solution because they enable the nodes to self-organise, use local area communication, and can combine their communication in an intelligent way with minimal increase in communication. I present a combined protocol and MAC scheduler to enable multiple service protocols to function in a WSN at the same time without causing inter-protocol interference. The scheduler is throughput optimal as long as the communication requirements of all of the protocols remain within the communication capacity of the network. I show that the scheduler improves a dissemination protocol’s performance by 35%. A bio-inspired synchronisation service is presented which enables wireless sensor nodes to self organise and provide a time service. Evaluation of the protocol shows an 80% saving in communication over similar bio-inspired synchronisation approaches. I then add an information dissemination protocol, without significantly increasing communication. This is achieved through the ability of our bio-inspired algorithms to combine their communication in an intelligent way so that they are able to offer multiple services without requiring a great deal of inter-node communication.Open Acces

Scheduling of flexible manufacturing systems integrating petri nets and artificial intelligence methods.

The work undertaken in this thesis is about the integration of two well-known methodologies: Petri net (PN) model Ii ng/analysis of industrial production processes and Artificial Intelligence (AI) optimisation search techniques. The objective of this integration is to demonstrate its potential in solving a difficult and widely studied problem, the scheduling of Flexible Manufacturing Systems (FIVIS). This work builds on existing results that clearly show the convenience of PNs as a modelling tool for FIVIS. It addresses the problem of the integration of PN and Al based search methods. Whilst this is recognised as a potentially important approach to the scheduling of FIVIS there is a lack of any clear evidence that practical systems might be built. This thesis presents a novel scheduling methodology that takes forward the current state of the art in the area by: Firstly presenting a novel modelling procedure based on a new class of PN (cb-NETS) and a language to define the essential features of basic FIVIS, demonstrating that the inclusion of high level FIVIS constraints is straight forward. Secondly, we demonstrate that PN analysis is useful in reducing search complexity and presents two main results: a novel heuristic function based on PN analysis that is more efficient than existing methods and a novel reachability scheme that avoids futile exploration of candidate schedules. Thirdly a novel scheduling algorithm that overcomes the efficiency drawbacks of previous algorithms is presented. This algorithm satisfactorily overcomes the complexity issue while achieving very promising results in terms of optimality. Finally, this thesis presents a novel hybrid scheduler that demonstrates the convenience of the use of PN as a representation paradigm to support hybridisation between traditional OR methods, Al systematic search and stochastic optimisation algorithms. Initial results show that the approach is promising

LIPIcs, Volume 248, ISAAC 2022, Complete Volume

LIPIcs, Volume 248, ISAAC 2022, Complete Volum